TitleAn optimized algorithm for detecting and annotating regional differential methylation.
Publication TypeJournal Article
Year of Publication2013
AuthorsLi, Sheng, Garrett-Bakelman Francine E., Akalin Altuna, Zumbo Paul, Levine Ross, To Bik L., Lewis Ian D., Brown Anna L., D'Andrea Richard J., Melnick Ari, and Mason Christopher E.
JournalBMC Bioinformatics
Volume14 Suppl 5
IssueSuppl 5
PaginationS10
Date Published2013
ISSN1471-2105
KeywordsAlgorithms, CpG Islands, DNA Methylation, Epigenomics, Genome, Genomics, High-Throughput Nucleotide Sequencing, Humans, Leukemia, Molecular Sequence Annotation, Sequence Analysis, DNA
Abstract

<p><b>BACKGROUND: </b>DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data.</p><p><b>RESULTS: </b>Here we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types.</p><p><b>CONCLUSIONS: </b>Our weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at http://code.google.com/p/edmr/.</p>

DOI10.1186/1471-2105-14-S5-S10
Alternate JournalBMC Bioinformatics
PubMed ID23735126
PubMed Central IDPMC3622633
Grant ListK08 CA169055 / CA / NCI NIH HHS / United States
R44HG005297 / HG / NHGRI NIH HHS / United States
R01HG006798 / HG / NHGRI NIH HHS / United States
R01 HG006798 / HG / NHGRI NIH HHS / United States
R01 NS076465 / NS / NINDS NIH HHS / United States
K08CA169055 / CA / NCI NIH HHS / United States
R01NS076465 / NS / NINDS NIH HHS / United States